{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ed729b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using PyTorch version: 2.5.1+cu124  Device: cuda\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "    \n",
    "print('Using PyTorch version:', torch.__version__, ' Device:', device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f758233",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "数据集加载完成，共有100个类别\n"
     ]
    }
   ],
   "source": [
    "# 定义预处理管道\n",
    "transform_train = transforms.Compose([\n",
    "    transforms.RandomCrop(32, padding=4),  # 随机裁剪\n",
    "    transforms.RandomHorizontalFlip(),     # 随机水平翻转\n",
    "    transforms.ToTensor(),                 # 转换为张量\n",
    "    transforms.Normalize(                  # 标准化\n",
    "        (0.5071, 0.4867, 0.4408),  # CIFAR-100的均值\n",
    "        (0.2675, 0.2565, 0.2761)   # CIFAR-100的标准差\n",
    "    )\n",
    "])\n",
    "\n",
    "transform_test = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize(\n",
    "        (0.5071, 0.4867, 0.4408), \n",
    "        (0.2675, 0.2565, 0.2761)\n",
    "    )\n",
    "])\n",
    "\n",
    "batch_size = 64\n",
    "\n",
    "# 下载并加载CIFAR-100训练集\n",
    "trainset = torchvision.datasets.CIFAR100(root='./data', train=True,\n",
    "                                          download=True, transform=transform_train)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
    "                                          shuffle=True, num_workers=2)\n",
    "\n",
    "# 下载并加载CIFAR-100测试集\n",
    "testset = torchvision.datasets.CIFAR100(root='./data', train=False,\n",
    "                                         download=True, transform=transform_test)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
    "                                         shuffle=False, num_workers=2)\n",
    "\n",
    "# CIFAR-100的类别标签\n",
    "classes = ['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', \n",
    "    'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', \n",
    "    'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', \n",
    "    'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', \n",
    "    'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', \n",
    "    'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion',\n",
    "    'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse',\n",
    "    'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear',\n",
    "    'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine',\n",
    "    'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose',\n",
    "    'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake',\n",
    "    'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table',\n",
    "    'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout',\n",
    "    'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman',\n",
    "    'worm']\n",
    "\n",
    "print(\"数据集加载完成，共有100个类别\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ce45caa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class BasicBlock(nn.Module):\n",
    "    expansion = 1\n",
    "    def __init__(self, in_channels, out_channels, stride=1):\n",
    "        super(BasicBlock, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1)\n",
    "        self.bn1 = nn.BatchNorm2d(out_channels)\n",
    "        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)\n",
    "        self.bn2 = nn.BatchNorm2d(out_channels)\n",
    "        self.shortcut = nn.Sequential()\n",
    "        if stride != 1 or in_channels != out_channels:\n",
    "            self.shortcut = nn.Sequential(\n",
    "                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),\n",
    "                nn.BatchNorm2d(out_channels)\n",
    "            )\n",
    "\n",
    "    def forward(self, x):\n",
    "        out = F.relu(self.bn1(self.conv1(x)))\n",
    "        out = self.bn2(self.conv2(out))\n",
    "        out += self.shortcut(x)\n",
    "        out = F.relu(out)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bf9f877",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class ResNet18_CIFAR100(nn.Module):\n",
    "    def __init__(self, num_classes=100):\n",
    "        super(ResNet18_CIFAR100, self).__init__()\n",
    "        self.in_channels = 64\n",
    "        \n",
    "        # 调整初始卷积层以适应32x32的CIFAR图像\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)  # 修改：使用3x3卷积，stride=1\n",
    "        self.bn1 = nn.BatchNorm2d(64)\n",
    "        self.relu = nn.ReLU(inplace=True)\n",
    "        # 移除原始ResNet中的maxpool层，避免过度下采样\n",
    "        \n",
    "        # 残差层组\n",
    "        self.layer1 = self._make_layer(64, 2, stride=1)\n",
    "        self.layer2 = self._make_layer(128, 2, stride=2)\n",
    "        self.layer3 = self._make_layer(256, 2, stride=2)\n",
    "        self.layer4 = self._make_layer(512, 2, stride=2)\n",
    "        \n",
    "        # 分类层\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n",
    "        self.fc = nn.Linear(512 * BasicBlock.expansion, num_classes)  # 输出类别数调整为100\n",
    "\n",
    "    def _make_layer(self, out_channels, num_blocks, stride):\n",
    "        layers = []\n",
    "        layers.append(BasicBlock(self.in_channels, out_channels, stride))\n",
    "        self.in_channels = out_channels * BasicBlock.expansion\n",
    "        for _ in range(1, num_blocks):\n",
    "            layers.append(BasicBlock(self.in_channels, out_channels))\n",
    "\n",
    "        return nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = self.bn1(x)\n",
    "        x = self.relu(x)\n",
    "        \n",
    "        x = self.layer1(x)\n",
    "        x = self.layer2(x)\n",
    "        x = self.layer3(x)\n",
    "        x = self.layer4(x)\n",
    "\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1f6373f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "net = ResNet18_CIFAR100().to(device)\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n",
    "# 记录训练过程的指标\n",
    "train_losses = []\n",
    "val_losses = []\n",
    "train_accuracies = []\n",
    "val_accuracies = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92249b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1: Train Loss: 3.817 | Train Acc: 10.99% | Val Loss: 3.785 | Val Acc: 14.35%\n"
     ]
    }
   ],
   "source": [
    "for epoch in range(50):  # loop over the dataset multiple times\n",
    "\n",
    "    net.train()  # 训练模式\n",
    "    running_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data[0].to(device), data[1].to(device)\n",
    "\n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        running_loss += loss.item()\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "\n",
    "    # 计算训练指标\n",
    "    train_loss = running_loss / len(trainloader)\n",
    "    train_accuracy = 100 * correct / total\n",
    "    train_losses.append(train_loss)\n",
    "    train_accuracies.append(train_accuracy)\n",
    "\n",
    "    # 验证阶段\n",
    "    net.eval()  # 评估模式\n",
    "    val_loss = 0.0\n",
    "    val_correct = 0\n",
    "    val_total = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for data in testloader:\n",
    "            images, labels = data[0].to(device), data[1].to(device)\n",
    "            outputs = net(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            \n",
    "            val_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            val_total += labels.size(0)\n",
    "            val_correct += (predicted == labels).sum().item()\n",
    "\n",
    "    # 计算验证指标\n",
    "    val_loss = val_loss / len(testloader)\n",
    "    val_accuracy = 100 * val_correct / val_total\n",
    "    val_losses.append(val_loss)\n",
    "    val_accuracies.append(val_accuracy)\n",
    "\n",
    "    print(f'Epoch {epoch+1:2d}: '\n",
    "          f'Train Loss: {train_loss:.3f} | Train Acc: {train_accuracy:.2f}% | '\n",
    "          f'Val Loss: {val_loss:.3f} | Val Acc: {val_accuracy:.2f}%')\n",
    "    \n",
    "    if((epoch+1) %5 == 0):\n",
    "        torch.save(net.state_dict(), f'./resnet18epoch{epoch}.pth')\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11bce8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# Loss曲线\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(train_losses, label='Train')\n",
    "plt.plot(val_losses, label='Validation')\n",
    "plt.title('Loss Curve')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "# 准确率曲线\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(train_accuracies, label='Train')\n",
    "plt.plot(val_accuracies, label='Validation')\n",
    "plt.title('Accuracy Curve')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy (%)')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5f30ef",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
